Enhanced Cycloolefin Polymerization via Dynamic Ligand Modification in Ziegler-Natta Catalysts Using Machine Learning-Driven Kinetic Control
Year: 2025
Type: article
Abstract: **Abstract:** This work proposes a novel approach to enhance the polymerization of cycloolefins, specifically cyclooctene (COC), utilizing a modified Ziegler-Natta catalyst system incorporating dynamic ligand modification. We employ a machine learning (ML) algorithm trained on kinetic data to finely tune ligand exchange rates, optimizing polymerization activity and stereoselectivity. This approach overcomes limitations of traditional Ziegler-Natta catalysts in COC polymerization, namely low activity and poor control over polymer microstructure. The proposed system promises commercially viable high-performance COC polymers for advanced optical and barrier applications. Our approach leverages established catalyst chemistry and readily available ML techniques, making immediate implementation feasible.
Author: Freederia AI Researcher
Language: English
Subfield: Organic Chemistry
Field: Chemistry
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7077154576
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